Sign Language System for Bahasa Indonesia (Known as SIBI) Recognizer using TensorFlow and Long Short-Term Memory

SIBI is used formally as a Sign Language System for Bahasa Indonesia. SIBI follows Bahasa Indonesia's grammatical structure, which makes it a unique and complex Sign Language System. The state of current research in SIBI is that it is possible to translate the alphabet, root words and numbers to text. This research focuses in recognizing inflectional words, which are root words and combination of prefix, infix and suffix. By separating the root words, prefix, infix and suffix, it was possible to use minimal feature sets. SIBI sequence data contains temporal dependencies, therefore Long Short-Term Memory (LSTM) is chosen as the machine learning model to use on this problem. The entire sequence of feature sets based on the SIBI inflectional word gestures is used as input. TensorFlow is used as development framework to make sure model can be easily deployed to a variety of devices, including smartphones. The best results were obtained using a 2-layer LSTM with 96.15% of accuracy for root words. The same model obtained an accuracy score of 78.38% with inflectional words. The model, however, still struggles in recognizing prefixes and suffixes correctly.